Summary

3D Particle Tracking for Noninvasive In Vivo Analysis of Synaptic Microtubule Dynamics in Dendrites and Neuromuscular Junctions of Drosophila

Published: May 12, 2020
doi:

Summary

This study presents a noninvasive intravital neuronal imaging strategy combined with a new software strategy to achieve automated, unbiased tracking and analysis of in vivo microtubule (MT) plus-end dynamics in the sensory dendrites and the neuromuscular junctions of Drosophila.

Abstract

Microtubules (MTs) play critical roles in neuronal development, but many questions remain about the molecular mechanisms of their regulation and function. Furthermore, despite progress in understanding postsynaptic MTs, much less is known about the contributions of presynaptic MTs to neuronal morphogenesis. In particular, studies of in vivo MT dynamics in Drosophila sensory dendrites yielded significant insights into polymer-level behavior. However, the technical and analytical challenges associated with live imaging of the fly neuromuscular junction (NMJ) have limited comparable studies of presynaptic MT dynamics. Moreover, while there are many highly effective software strategies for automated analysis of MT dynamics in vitro and ex vivo, in vivo data often necessitate significant operator input or entirely manual analysis due to inherently inferior signal-to-noise ratio in images and complex cellular morphology.  To address this, this study optimized a new software platform for automated and unbiased in vivo particle detection. Multiparametric analysis of live time-lapse confocal images of EB1-GFP labeled MTs was performed in both dendrites and the NMJ of Drosophila larvae and found striking differences in MT behaviors. MT dynamics were furthermore analyzed following knockdown of the MT-associated protein (MAP) dTACC, a key regulator of Drosophila synapse development, and identified statistically significant changes in MT dynamics compared to wild type. These results demonstrate that this novel strategy for the automated multiparametric analysis of both pre- and postsynaptic MT dynamics at the polymer-level significantly reduces human-in-the-loop criteria. The study furthermore shows the utility of this method in detecting distinct MT behaviors upon dTACC-knockdown, indicating a possible future application for functional screens of factors that regulate MT dynamics in vivo. Future applications of this method may also focus on elucidating cell type and/or compartment-specific MT behaviors, and multicolor correlative imaging of EB1-GFP with other cellular and subcellular markers of interest. 

Introduction

Cells organize to form functional structures through the coordination of intra- and intercellular changes via morphogenesis. A remarkable example of morphogenesis is the development of the highly specialized neuronal structure. Neurons display remarkable polarization, in which they extend two structurally and functionally distinct types of processes, dendrites and axons1, which can achieve immense lengths. The complexity of neuronal development arises not only from the sheer size of dendrites and axons but also from the difficulty in forming their intricately branched geometries2,3. Neuronal morphogenesis and its consequences in learning and memory4 motivate the ongoing investigation of both its genetic control and the underlying cell biological mechanisms. Such mechanisms include, but are not limited to, intracellular membrane transport and the many cytoskeletal rearrangements needed for changes in neuronal morphology1,2,3.

Studies of neuronal morphogenesis have produced a variety of advanced visualization techniques. Static methods, such as electron microscopy or fluorescence microscopy of fixed probes, are widely used to perform high-resolution morphological and structural analysis. However, besides the artifacts that are inevitable to any preservation method, static visualization cannot capture the dynamic changes that underpin morphogenesis. Thus, many pivotal insights originated from time-lapse fluorescence microscopy of living tissues. Early work by Lichtman and colleagues5,6,7 utilized in vivo imaging of the mammalian nervous system to investigate axon regeneration/degeneration, organization of synaptic components, and long-range axonal transport. Furthermore, seminal studies in primary neuronal explants were critical to establishing the importance of microtubule (MT) dynamics to axonal elongation and motility8,9. Crucially, early neuronal explant studies established the use of fluorescently-tagged end-binding family proteins (EBs) to gain invaluable insights into MT plus-end dynamics in developing neurons at the level of individual MT polymers10. These studies arose from observations that the EB family member EB1 preferentially localizes to MT plus ends11 in S. cerevisiae12 and in cultured cells13. Since then, EB1 and other plus tip tracking proteins (+TIPs)14,15 have been widely used in in vivo studies of MT dynamic instability16, including in the context of neuronal development17.

Drosophila is a powerful model for in vivo imaging studies of MT dynamics during neuronal development due to the vast genetic and imaging tools available for fly studies18,19 as well as the similarities in structure and function between Drosophila and vertebrate neurons1. A key early study of the neuromuscular junction (NMJ) of Drosophila larvae performed repeated noninvasive imaging of a fluorescent membrane marker through the translucent cuticle of intact animals to document presynaptic terminal morphogenesis20. Using a similar method to image whole, live Drosophila larvae, an initial demonstration of subcellular, particle-level analysis of processive movement of motor cargos in the axons was provided21. More recently, meticulous studies by Rolls and colleagues in the sensory dendrites of intact Drosophila larvae22,23,24,25,26,27 characterized postsynaptic MT plus-end dynamics by performing particle tracking and analysis of green fluorescent protein (GFP)-tagged EB1. Such studies in Drosophila22,23,24,25,26,27 and other systems28,29,30,31,32 have significantly advanced understanding of single-polymer behavior of MT plus ends in the dendrites of developing neurons33.

Despite the impressive in vivo studies of postsynaptic MT dynamics22,23,24,25,26,27,28,29,30,31, there have been far fewer comparable studies of presynaptic MT dynamics at the developing axon terminal. MT dynamics at the Drosophila larval NMJ has been studied using fluorescent speckle microscopy (FSM) and fluorescence recovery after photobleaching (FRAP)34. These techniques evaluate the overall tubulin kinetics but not the behavior of individual MT plus ends. As of this writing, there has been one sole investigation of individual MT plus ends at the Drosophila NMJ: This study combined live time-lapse imaging with manual analysis of kymographs to characterize a population of dynamic, EB1-GFP labeled "pioneering MTs" that appeared distinct from a broader population of stabilized MTs35. This lack of research on presynaptic MT dynamics may be due at least in part to anatomy: While it is relatively straightforward to obtain images of dendrites due to their proximity to the larval cuticle, NMJs are obstructed by other tissues, making it challenging to acquire images with sufficient signal-to-noise ratio for particle-level analysis. Nonetheless, given the well-established importance of the presynaptic MTs to synaptic morphogenesis and stabilization36, as well as their links to neurodevelopmental and neurodegenerative disorders37, bridging this gap between understanding of pre- and postsynaptic MTs is likely to yield invaluable insights. 

An additional challenge to the analysis of in vivo MT dynamics in general, in contrast to in vitro or ex vivo analysis, is the limited automated software tools that can extract dynamics parameters from in vivo data. Presently, one of the most popular and powerful techniques for analysis of +TIP-labeled MT plus ends is plusTipTracker38,39, a MATLAB-based software that allows automated tracking and analysis of multiple dynamics parameters. Notably, plusTipTracker measures not only MT growth but also shrinkage and rescues: while +TIP labels such as EB1-GFP only associate with growing plus ends, plusTipTracker can algorithmically infer shrinkage rates and rescue events. However, while plusTripTracker has been very successfully applied to many contexts, including previous multiparametric analysis of ex vivo MT dynamics in Drosophila S2 cells40, plusTipTracker is not optimal for analysis of in vivo data given their lower signal-to-noise ratio. As a result, in vivo studies of plus-end dynamics at dendrites22,23,24,25,26,27 and at the NMJ35 of Drosophila have relied on manual generation and analysis of kymographs using software such as ImageJ41, or on semiautomated strategies that involve numerous human-in-the-loop components.

This study presents an experimental and analytical workflow that reduces the experimental and analytical overhead required to perform noninvasive polymer-level analysis of presynaptic MT dynamics in both sensory dendrites and the motor axon terminal of Drosophila third-instar larvae. The protocol utilizes immobilized, intact larvae and therefore avoids injuries known to trigger stress responses as well as other nonphysiological conditions that might perturb in vivo MT dynamics. To label dynamic MT plus-ends, EB1-GFP is pan-neuronally expressed using the Gal4/UAS system42, allowing visualization of MTs at both dendrites and NMJ with a single driver. While some early steps are inevitably subject to human decision-making, such as the selection of animal specimens and identification of regions to image, the steps following data acquisition are largely automated. Crucially, optimization of a new software enabled automated, unbiased analysis requiring minimal human input. While other particle tracking methods are available43,44,45, this study utilizes a proprietary software because it was algorithmically well-suited to address the particular challenges of this particular dataset. The software is now available to users for a variety of applications. Specifically, the use of coherence-enhancing diffusion filtering46 is integral to automated segmentation and background removal, and custom algorithms are implemented specifically to automate particle detection and tracking. This strategy could effectively handle the low signal-to-noise ratio inherent to the data in this study, as well as other challenges, such as movement of EB1-GFP comets through different focal planes. While it is not feasible to exhaustively test the performance of this software against all other particle analysis software, the performance of the present strategy equaled or approached the standard human performance. Furthermore, to the authors’ knowledge, there has been no other software specifically trained on in vivo data from sensory dendrites and the presynaptic terminal. Given that the performance of image analysis algorithms is often highly specific to the data they were designed for and that generalized computer vision is not yet possible, it is expected that training the described software to the specific in vivo data of interest is the most algorithmically sound approach.

Given the extensive work on dendritic MTs22,23,24,25,26,27 as well as the consistent quality of data that can be acquired from this system, the image acquisition and software analysis strategy was first validated in Drosophila sensory dendrites. Importantly, it was found in dendrites that the use of different neuronal Gal4 drivers, even in otherwise identical wild type backgrounds, results in significant differences in EB1-GFP dynamics due to differences in genetic background, emphasizing the importance of using a single Gal4 driver for consistent results. This strategy was next used for multiparametric analysis of EB1-GFP dynamics at the presynaptic terminal of the NMJ. To further illustrate the investigative value of this method, this imaging and software strategy was used to assess both pre- and postsynaptic EB1-GFP dynamics following knockdown of dTACC, the Drosophila homolog of the highly conserved TACC (transforming acidic coiled coil) family47,48. Prior work in Drosophila S2 cells40, as well as work by Lowery and colleagues in the Xenopus growth cone49,50,51, has shown that TACC family members regulates MT plus-end dynamics. Furthermore, recently reported evidence from confocal and super-resolution immunofluorescence imaging showed that dTACC is a key regulator of presynaptic MTs during neuronal morphogenesis52, raising the question of whether dTACC regulates live MT dynamics. This report demonstrates a method that can indeed detect differences in live MT behaviors upon dTACC knockdown. Thus, this study presents an in vivo method that can effectively identify and characterize key regulators of MT dynamics within the developing neuron, particularly in the presynaptic compartment. 

Protocol

1. Generation of Drosophila specimens Select a suitable MT plus-end marker. This study utilized GFP-tagged EB1, a well-characterized plus-end marker with a strong, clear signal11,12. Alternatives include other +TIPs such as EB310,13, CLASP/Orbit53, and CLIP-17054. Obtain or generate flies with the MT marker under control of a UAS promot…

Representative Results

Flies were raised from stable stocks that constitutively express the UAS-EB1-GFP transgene either pan-neuronally (elaV-Gal4; UAS-EB1-GFP)58,59 or in sensory neurons (221-Gal4; UAS-EB1-GFP)60,61. EB1 was chosen for this study because it specifically localizes to growing ends and dissociates immediately upon pause and shrinkage14,15</sup…

Discussion

This paper discusses a protocol to perform noninvasive intravital imaging of MT dynamics in the dendrites and at the NMJ of during development. Human input is required during the experimental steps, such as in selecting animals to image, and may introduce bias in the data collection process that cannot be reasonably removed. Thus, a key goal of the protocol is to minimize bias wherever possible by performing automated analysis with a new software (section 5) that was optimized to handle the low signal-to-noise ratio inhe…

Divulgaciones

The authors have nothing to disclose.

Acknowledgements

We thank our colleagues in the Van Vactor lab and at DRVision in addition to Drs. Max Heiman, Pascal Kaeser, David Pellman, and Thomas Schwarz for helpful discussion. We thank Dr. Melissa Rolls for generously providing the elaV-Gal4; UAS-EB1-GFP; UAS-Dcr2 and 221-Gal4; UAS-EB1-GFP stocks used in this study. We thank Drs. Jennifer Waters and Anna Jost at the Nikon Imaging Center at Harvard for light microscopy expertise. This work is funded by the National Institutes of Health (F31 NS101756-03 to V.T.C., SBIR 1R43MH100780-01D to J.S.L.).

Materials

1.5 mL microcentrifuge tube Eppendorf 21008-959 Sample preparation
1000 µL TipOne pipette tips USA Scientific 1111-2721 Sample preparation
200 µL TipOne pipette tips USA Scientific 1120-8710 Sample preparation
221-Gal4 flies Bloomington Drosophila Stock Center (US) 26259 Drosophila genetics/crosses
60x Objective Lens Nikon Plan Apo 60x Oil Image acquisition
6-well plate BD Falcon 353224 Sample preparation
Agar MoorAgar 41084 Drosophila food
Aivia DRVision LLC Optimized as part of this study
Chloroform (stabilized with amylenes) Sigma-Aldrich C2432 Sample preparation
CO2 blowgun (for selection of flies for crosses) Genesee 54-104 Drosophila genetics/crosses
CO2 bubbler (for selection of flies for crosses) Genesee 59-180 Drosophila genetics/crosses
Cooled CCD camera Hamamatsu ORCA-R2 Image acquisition
Cornmeal Genesee 62-101 Drosophila food
Distilled Water Drosophila food
Double-sided tape Scotch Sample preparation
Drosophila vials Genesee 32-109 Drosophila food
Droso-plugs (foam plugs for vials) Genesee 59-200 Drosophila food
Dumont #5 Biologie Inox Forceps Fine Science Tools 11252-20 Sample preparation
elaV-Gal4;UAS-EB1-GFP;UAS-Dcr2 flies Gift of Melissa Rolls (Penn State University) N/A Drosophila genetics/crosses
Ethanol (95%) VWR 75811-022 Drosophila food
Fiber optic illuminator/light source for stereomicroscope Nikon NI-150 Sample preparation
Flypad (for selection of flies for crosses) Genesee 59-172 Drosophila genetics/crosses
Forma Environmental Chamber/Incubator ThermoFisher 3940 Drosophila genetics/crosses
Halocarbon oil 700 Sigma-Aldrich H8898 Sample preparation
Immersion Oil Nikon MXA22168 Image acquisition
Kimwipe Delicate Wipes Fisher Scientific 34120 Sample preparation
Laser Merge Module Spectral Applied Research LMM-5 Image acquisition
Light Source for Confocal Lumencor SOLA 54-10021 Image acquisition
MetaMorph Microscopy Automation & Image Analysis Software Molecular Devices Image acquisition
Micro Cover Glasses, Square, No. 1 1/2 (#1.5) VWR 48366-205 Sample preparation
Motorized inverted microscope with Perfect Focus System Nikon TI-ND6-PFS-S Image acquisition
Motorized stage and shutters Prior Proscan III Image acquisition
Multi-purpose scissors Scotch MMM1428 Sample preparation
Nail Polish Sally Hansen 784179032016 074170382839 Sample preparation
Optical Filter Chroma ET480/40m Image acquisition
P1000 Pipetman Gilson F123602 Sample preparation
P200 Pipetman Gilson F123601 Sample preparation
PBS (10X) ph 7.4 ThermoFisher 70011044 Sample preparation
Propionic Acid Fisher A258-500 Drosophila food
Spinning disk confocal scanner unit Yokagawa CSU-X1 Image acquisition
Stereomicroscope Nikon SMZ800N Sample preparation
Sugar (Sucrose) Genesee 62-112 Drosophila food
Superfrost Slide VWR 48311-600 Sample preparation
Tegosept Genesee 20-258 Drosophila food
UAS-dtacc-RNAi flies Vienna Drosophila Resource Center (Vienna, Austria) VDRC-101439 Drosophila genetics/crosses
Vaseline petroleum jelly WB Mason DVOCB311003 Sample preparation
Winsor & Newton Brush Regency Gold 520, Size 0 Staples 5012000 Drosophila genetics/crosses
Yeast VWR Torula Yeast IC90308580 Drosophila food
Yokogawa dichroic beamsplitter Semrock Di01-T405/488/568/647-13x15x0.5 Image acquisition

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Chou, V. T., Yesilyurt, H. G., Lai, H., Long, J. B., Arnes, M., Obbad, K., Jones, M., Sasaki, H., Lucas, L. A., Alworth, S., Lee, J. S., Van Vactor, D. 3D Particle Tracking for Noninvasive In Vivo Analysis of Synaptic Microtubule Dynamics in Dendrites and Neuromuscular Junctions of Drosophila. J. Vis. Exp. (159), e61159, doi:10.3791/61159 (2020).

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